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DBMS > Databend vs. RDF4J vs. Spark SQL

System Properties Comparison Databend vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRDF storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.30
Rank#287  Overall
#130  Relational DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
rdf4j.orgspark.apache.org/­sql
Technical documentationdocs.databend.comrdf4j.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabend LabsSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release202120042014
Current release1.0.59, April 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustJavaScala
Server operating systemshosted
Linux
macOS
Linux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeyesyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesnoyesno
SQL infoSupport of SQLyesnoSQL-like DML and DDL statements
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyesno
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesnonenoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnonenonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesnono

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DatabendRDF4J infoformerly known as SesameSpark SQL
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